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Hidden layer: Difference between revisions
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==Training Hidden Layers== | ==Training Hidden Layers== | ||
Training a neural network with hidden layers involves adjusting the weights and biases of nodes within each layer to minimize the difference between predicted output and actual output. This is typically accomplished using an optimization algorithm like gradient descent, which alters weights and biases according to the steepest descent of the loss function. | Training a neural network with hidden layers involves adjusting the [[weights]] and [[biases]] of nodes within each layer to minimize the difference between predicted output and actual output. This is typically accomplished using an optimization algorithm like gradient descent, which alters weights and biases according to the steepest descent of the loss function. | ||
During training, a neural network learns to adjust the weights and biases of nodes within each hidden layer to represent increasingly complex patterns in data. As such, training a neural network with multiple hidden layers can be computationally expensive and necessitate large amounts of training data. | During training, a neural network learns to adjust the weights and biases of nodes within each hidden layer to represent increasingly complex patterns in data. As such, training a neural network with multiple hidden layers can be computationally expensive and necessitate large amounts of training data. | ||